Hmm-based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer_zebros Ieee Projects

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HMM-BASED HUMAN FALL DETECTION AND

PREDICTION METHOD USING TRI-AXIAL

ACCELEROMETER

ABSTRACT

Falls in the elderly have always been a serious medical and social problem.

To detect and predict falls, a hidden Markov model (HMM)-based method using tri-

axial accelerations of human body is proposed. A wearable motion detection device

using tri-axial accelerometer is designed and realized, which can detect and predict falls

based on tri-axial acceleration of human upper trunk. The acceleration time series (ATS)

extracted from human motion processes are used to describe human motion features,

and the ATS extracted from human fall courses but before the collision are used to train

HMM so as to build a random process mathematical model. Thus, the outputs of HMM,

which express the marching degrees of input ATS and HMM, can be used to evaluate

the risks to fall. The experiment results show that fall events can be predicted 200–400

ms ahead the occurrence of collisions, and distinguished from other daily life activities

with an accuracy of 100%.

EXISTING SYSTEM

The existing system is not a real time system. This is fully mat lab simulation. But

we propose the new real time fall detection system. The existing systems are used to

algorithm based simulation. This system is more delay and not efficiency.

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BLOCK DIAGRAM

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PROPOSED SYSTEM

The angle errors calculated from tri-axial acceleration maybe one of the

reasons for misdetection of accelerometer based detection system, since their outputs

consist of not only body accelerations but also the gravity. And the acceleration

information at one instance is not sufficient to describe human motions, since they are

processes. As for gyroscope based detection system, it brings significant errors to the

calculated angular acceleration and angular position through differential and integral

operations, since those low-cost gyroscopes suffer from time-varying zero shift

seriously. Although these errors can be compensated using magnetograph, it takes

calculation too complex to be implanted on a single chip in real-time applications.

Hence, it is expected that to predict and detect fall events accurately with thresholds

methods, a tri-axial accelerometer and gyroscope are needed.

This system describes a reliable human fall detection and prediction

method using HMM and tri-axial accelerometer, through analyzing the features of

human motion series during fall processes. First, acquire tri-axial acceleration at human

upper trunk from fall processes and other daily life activities. Second, extract features

that describe the movements during a series of short time periods by turns to make up

ATS, which characterize motion processes. Then, study the features of ATS from the

course that before the collision of body with lower subjects in fall processes as training

samples to build HMM, whose outputs express the marching degree of input ATS and

HMM, thus it can be applied to evaluate the risks to fall. Finally, we set thresholds by

compiling statistics of the outputs from different motion processes to detect and predict

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fall events. The experiment results show that this method can predict falls in 200∼400ms

before the impact and can also distinguish fall events from other daily life activities

accurately.

ADVANTAGE

Low cost

Low power

Less delay

APPLICATIONS

Medical applications

Sports applications

Child and old people use the system

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SOFTWARE BASED HARDWARE BASED

Networking VLSI

Data Mining Mat lab

Grid Computing Robotics

Network Security Embedded

Image Processing Bio Medical

Web Applications Power Systems

Mobile Computing Power Electronics

Software Engineering Java with Embedded

Cloud Computing Android

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0th Review 1st Review

Abstract Existing System Disadvantages Proposed System Advantages Objective System Requirements System Architecture

Literature Survey Module List Module Description Data Flow Diagram Level DFD Module Wise DFD Problem Definition Review Document Explanation

2nd Review 3rd Review

Use case Diagram Class Diagram Collaboration Diagram Sequence Diagram Activity Diagram Testing & test cases Partial Code Screenshot for First two

module Review Document Explanation

Conclusion References Future Enhancement 65% code (Executable Format) Required Software Review Document Explanation

Final Review

Complete Code with Enhancement

Final Document (University Standard Format)

Complete Explanation for Project Concept & Code

Documents

Hmm-based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer_zebros Ieee Projects